Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

The segmentation and classification of liver tumours are crucial in medical imaging, aiding early detection and treatment planning for liver diseases. Deep learning-based liver lesion segmentation has the potential to enhance the precision and effectiveness of liver disease detection. Recent studies have shown promising results in liver cancer prediction using convolutional neural network (CNN)-based techniques. This work proposes a Multiparameterised Inception v3 CNN to improve feature extraction for liver cancer prediction. Additionally, Krill Herd optimisation (KHO) optimisation can be applied to identify ideal hyperparameters, further enhancing the system's performance. By integrating KHO, the proposed model can achieve higher accuracy in predicting liver cancer, benefiting both patients and medical professionals. The study, conducted on the liver tumour segmentation (LiTS) dataset, evaluates accuracy, sensitivity, and specificity, with the MIV3CNNKHO model achieving 96% accuracy, 0.96 sensitivity, and 0.94 specificity. The implementation was done using Jupyter Notebook, with Python as the programming language. The optimised system offers an improved solution for liver cancer detection and prognosis, making it a valuable tool in medical imaging.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

This paper aims to discover in-depth knowledge of the design technology, material used, and clinical use of dynamic response prosthetic feet in rehabilitating lower limb amputees. Studies were done using electronic databases such as PubMed, Google Scholar, SCOPUS, and Research Network 2010-2024. Out of 162 papers, 43 papers relevant studies were included. Significant advances in the research and development of prosthetic legs over the last two decades have improved the functioning and quality of life of many lower-limb amputees living in industrialised countries. The disadvantage of this new RandD is that most end users live in developing countries and cannot benefit from this new technology due to the cost, durability, maintenance, and availability of these components. Research is needed to design and develop cost-effective prosthetic legs that meet economic, environmental, and physical standards and withstand adverse climatic and working conditions.

Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus strain, is a significant threat worldwide due to its fast spreading among people. Currently, many vaccines have been used for prevention and to improve the immune system of people. Still, these vaccines are not prevented enough, and many severe side effects exist, including death in many patients. In this study, firstly, we attempted to identify the target gene responsible for driving the immune response against viruses through network and functional analyses; secondly, potential active components present in the ingredients of Kabasura Kudineer (15 herbal plants components) were identified, and its effect against COVID-19; thirdly, the transcription factor (IRF3) and active compounds of Kabasura Kudineer were involved in virtual screening. Pharmacokinetic properties were compared after predicting the ADMET values of Costunolide, Lupeol, Vazegepant and Methylprednisolone. The overall work revealed that the selected active compounds increase the transcription factor enhancement regarding the immune response.

Magnolia champaca extracts have various pharmacological properties like antifertility, antibacterial, anti-inflammatory, and antioxidant activities. However, the pharmacological activities of Magnolia champaca leaf essential oils are still unknown. This study aimed to examine the antidiabetic, antifertility, anti-inflammatory, and antioxidant properties of the phytoconstituents of MCLEO using computational biological approaches. After analysing 65 compounds, 28 satisfied ADMET properties and were selected for molecular docking to provide mechanistic insights into α-Glucosidase, Estrogen Receptor-α, Tumour Necrosis Factor- α, and Xanthine Oxidase inhibition. Crystallographic structures with PDB ID: 8D43, 1R5K, 5MU8, and 2CKJ of α-G, ER-α, TNF-α, and XO, respectively were used as models for molecular docking. The result showed that cis-Muurola-3,5-diene and Selin-11-en-4-α-ol showed docking scores of -8.489 and -9.146 against α-G and ER-α, respectively. δ-Cadinene showed docking scores of -7.951 and -8.268 against TNF-α and XO. This is the first in silico study to discover prospective bioactive compounds from MCLEO which could be useful in developing novel and effective medications.